SpaceXSI, AT&T, Verizon, T-Mobile and Beauty Contests

AT&T, Verizon and T-Mobile have been under pressure since SpaceXSI suggested it would be competing in the mobile phone business, and has followed up by acquiring 800-MHz low-band spectrum ideally suited to inside-building connectivity, an important consideration since satellite-to-phone only works when there is line-of-sight. 

According to Elon Musk, the purchase is "the last critical piece of the spectrum puzzle needed for SpaceX to provide complete phone coverage in America".

On October 9, 2026, T-Mobile US shares plunged 13 percent. the company's worst single-day drop since 2013.

AT&T shares fell approximately almost 10 percent, the worst single-day drop since 2000. Meanwhile, Verizon Communications declined about nine percent,  Verizon's worst single-day loss since July 2002.

Skeptics will argue the impact is out of proportion to the nature of the threat, as SpaceXSI still has to acquire or build a terrestrial transmission infrastructure. 

The impact on SpaceXSI was the opposite. Based on SpaceXSI's current market capitalization of $2.14 trillion, 3.5 percent of its market value is approximately $74.94 billion. So it might be argued that the 3.7-percent increase in market valuation on Oct. 9, 2026, which was about $75 billion, reflects the $8 billion investment in spectrum. 

In other words, SpaceXSI valuation increased about 10 times more than the cost of spectrum acquisition. 

On Oct. 9, SpaceXSI gained $27 billion, even using the low figure of 1.24 percent increase for the day.

The mobile service providers lost about $50 billion. 

You might argue that is an example of the “Keynesian beauty contest” at work. The Keynesian beauty contest is an economic and behavioral finance concept introduced by John Maynard Keynes in 1936 to explain how speculative markets and asset prices fluctuate.

The basic idea is that investors buy some stocks not because of strong company fundamentals, but because they believe others will buy them and drive the price higher.

The analogy is a beauty contest in which judges are rewarded for selecting the most popular choices among all judges, rather than those they may personally find the most attractive. 

As it pertains to SpaceXSI, at least some investors arguably price the company not on its current cash flows or fundamentals, but on what they believe other market participants will pay. 

It is, in other words, a narrative-driven market. We saw this during the dot-com bubble. But it has happened before then, as well. We call these periods “manias” or “bubbles.” 

I’m not arguing we are in a similar bubble at the moment. Maybe we are, maybe we will be, but it is not certain we are at the moment. 

Instead, we do see a beauty contest logic at work, where valuation is less on fundamentals and more on expectations. It might be unfair, but SpaceXSI currently benefits from the beauty contest, while the big three mobile service providers suffer.

Era and Mania	The "Prettiest Face" (The Hype Asset)	The Real Underlying Catalyst	The Beauty Contest Driver	The Collapse Trigger
1636–1637 Tulip Mania	Rare, variegated tulip bulbs (e.g., Semper Augustus).	The introduction of exotic flowers to the Dutch merchant class and early futures contract innovations.	Buyers knew a single bulb couldn't generate income. They bought purely because the broader Dutch public was highly coordinated in bidding up bulb futures, ensuring a "greater fool" would buy it next.	In February 1637, buyers at a routine auction in Haarlem simply failed to show up, breaking the psychological chain of assumed demand.
1720 South Sea Bubble	Shares of the South Sea Company.	A British government debt-for-equity swap scheme wrapped in a theoretical monopoly over South American trade routes.	Investors recognized that actual trade was non-existent. However, because the King and high-ranking politicians were buying and promoting it, citizens bought shares assuming the royal endorsement would force the public to keep bidding it up.	The company's own insiders realized the stock was dramatically overvalued and quietly began liquidating their positions, sparking a mass panic.
1840s Railway Mania	Shares of newly proposed railway companies.	The industrial revolution and the British government relaxing rules on corporate formation, combined with cheap credit.	While railways were revolutionary, the market funded thousands of overlapping, impossible routes. Investors didn't look at track viability; they chased the "railway" tag because newspaper columns constantly validated it.	The Bank of England raised interest rates to combat inflation, draining the cheap credit that speculators relied on to buy shares on leverage.
1920s The Roaring Twenties	Industrial conglomerates, radio technology stocks, and investment trusts.	Widespread adoption of electricity, automobiles, mass media, and the creation of retail margin accounts.	Speculators freely admitted stocks were expensive. They bought because loose margin requirements (borrowing up to 90% of the stock price) meant a massive wave of new retail money was constantly entering the market.	Corporate earnings began cooling in mid-1929, leading to minor margin calls that snowballed into the catastrophic Black Thursday crash of October 1929.
1997–2001 Dot-com Bubble	Any company with a ".com" suffix or internet business plan.	The commercial birth of the World Wide Web and massive telecom network buildouts.	Traditional valuation metrics (P/E ratios) were abandoned. Investors bought companies with zero revenue because they knew institutional mutual funds were suffering from intense FOMO and felt forced to chase internet listings.	Major tech backbones (like Cisco and Intel) reported spending slowdowns, revealing that startup capital was burning out, which ended the assumption of infinite demand.
2024–Present AI Infrastructure Boom	Hardware providers (GPUs), liquid cooling firms, and next-generation data center networks.	The emergence of highly scalable Large Language Models (LLMs) requiring massive parallel computing power.	High-frequency trading algorithms and retail momentum traders target companies based on the size of their "AI capital expenditures." Traders buy not for the immediate dividend yield, but because tech giants are locked in an arms race and must keep buying hardware.	Highly variable; heavily tied to micro-catalysts, structural demand shifts (such as ultra-efficient compute open-source models), or data center budget fatigue.

Is AI Infra Customer Concentration a Bug or a Feature?

Common sense suggests that any supplier of goods or services is at risk when the customer base is highly concentrated. Indeed, we might argue that buyer diversification is a core strategic objective for any supplier of goods or services.

Unfortunately, high customer concentration is a basic feature of high-performance computing as a service, leading to high concentration on the supply side as well. And that leads to a high concentration of earnings growth in public markets as well. 

Perhaps few like this state of affairs, as it increases market risk for just about everyone in the investing value chain. But it really cannot be helped. 

Customer concentration, often defined as having a single customer represent 10 percent or more of revenue, or top three to five customers representing 30 percent  to 50 percent of sales, creates vulnerabilities:

• The "single point of failure" (revenue shock if a key buyer defects)

• Squeezed profit margins (buyers know they have leverage)

• Asymmetric counterparty and credit risk if a single buyer defaults

• Operational and capacity misallocation to the few large buyers

• Higher cost of capital, as lenders and equity investors view customer concentration as a risk. 

But the current artificial intelligence infrastructure market is skewed to a few customers supplying high-performance computing as a service (Google, Amazon Web Services, Azure, Oracle, SpaceXSI)

At the same time, the AI infrastructure boom is producing:

• A relatively small group of companies capturing a disproportionate share of incremental earnings growth in the public equity market

• A similarly small group of hyperscalers  responsible for a large share of demand for advanced AI computing infrastructure.

Micron alone accounts for 19 percent of all earnings growth in the S&P 500 in the third quarter of 2026 and Nvidia accounts for 15 percent, for example. That’s earnings growth, not the share of total market earnings. 

source: Seeking Alpha

Goldman analysts say the top 10 contributors account for approximately 68 percent of growth. So Goldman estimates that AI infrastructure companies (excluding hyperscalers) will account for 54 percent of third-quarter earnings growth.

Hyperscalers will contribute another 19 percent. Together, AI infra suppliers and hyperscalers account for 73 percent of earnings growth.

That will worry just about everyone. But such concentration reflects scale economics in the frontier AI model part of the value chain; the inference as a service value chain and the AI infra value chain. 

 

 

source: Seeking Alpha

There arguably are several reasons why the concentration exists:

• scale economies favor large buyers

• capital access matters

• utilization matters

• complementary assets matter.

Such characteristics are common in capital-intensive industries such as telecommunications, aerospace, semiconductor fabrication and electricity generation. 

source: J.P. Morgan 

In some sense, we might argue that concentration is a feature, not a bug. That is not to discount the risks. Everything hinges on eventual outcomes across the economy, especially the ability of the frontier model developers and computing as a service suppliers to demonstrate economic value for their costumers, leading to subsequent revenue growth for the hyperscalers. 

So concentration, in and of itself,  is not itself a reason to reject the AI infrastructure opportunity. Many other successful industries also are capital intensive and concentrated on the buyer and supplier sides. 

But such concentration also does magnify risk. A spending slowdown by the  hyperscalers will disrupt market valuations of firms in the industry. 

The wisdom of current investment pacing will be determined by the return on the capital being deployed, of course.

But buyer or supplier concentration, in and of itself, is not necessarily a bug. It is a feature of some industries, where scale economies are required.

General-Purpose Technologies Generally Do Come with Job Concerns

At least one study suggests that Millennials use artificial intelligence tools more than Generation Z. According to a survey conducted by Channel V Media, 47 percent of Millennials use generative AI, compared to 44 percent of Gen Z, 42 percent of Gen X and 32 percent of Baby Boomers.

According to that survey, Gen Z is the generation most anxious about AI and most afraid of losing its own job to it. 

source: Channel V Media 

But history might suggest such initial hesitations must simply be dealt with, as there is little, if any, precedent for people being able to opt out or avoid the impact of new general-purpose technologies. Non-adoption, like it or not, is rarely, if ever, widely possible. 

Technology	Initial worker reaction	Who tended to adopt fastest?	What happened to the "avoiders"?	Strategic lesson
Steam power	Fear of mechanization and displacement; Luddites famously attacked machinery in some industries	Firms and workers whose jobs could exploit mechanization	Avoiding machines did not prevent mechanization; occupations and industries changed	You couldn't opt out of the production system
Electricity	Initially viewed as an industrial substitute for existing power systems	Firms reorganizing factories around electric motors	Firms that merely substituted electric motors for steam got less benefit than firms that redesigned production	Adoption matters, but complementary organizational change matters more
Telephone	Concerns about disruption of existing communications and occupations	Businesses and professionals with high communication needs	Nonusers increasingly faced a disadvantage in a connected economy	Network technologies reward participation
Personal computer	Significant worker anxiety about clerical/administrative automation	Younger, more educated and occupationally exposed workers disproportionately	Computer nonusers increasingly concentrated in lower-wage occupations	Avoidance could protect a particular task, but not necessarily the career
Internet	Similar fears about retail, media, travel agencies, newspapers, etc.	Younger and more educated workers initially; adoption subsequently became nearly universal	Non-adoption increasingly imposed an economic and social penalty	The technology eventually became infrastructure rather than an optional skill
GenAI	Explicit fear that the technology will eliminate one's occupation	Younger workers and highly exposed occupations are among the earliest users	Avoidance may preserve some skills temporarily but risks losing complementary AI skills	Learning to use AI may be the modern equivalent of learning the PC

In all likelihood, the data simply reflects the fact that AI is arriving, and quite possibly eliminating some entry-level jobs, just as many Gen Zers are entering the work force.

Frontier Model Revenue Strategies Start to Differentiate

As investors increasingly demand proof that language model suppliers have a clear path to monetization, the strategies have diverged. 

OpenAI and Anthropic monetize the model directly, especially through enterprise application programming interface usage, with consumer subscriptions as a second source.

Google monetizes Gemini indirectly in search advertising but also directly in Google Cloud, Workspace subscriptions and Gemini consumer subscriptions.

Meta largely gives models away and monetizes the resulting engagement through its advertising business.

xAI combines subscriptions, advertising, API and data licensing. 

Mistral and Cohere emphasize enterprise contracts, customization and private deployments.

Amazon uses models such as Nova primarily to make AWS more valuable, rather than relying on selling a chatbot as a standalone business.

Model	Who ultimately pays?	What is being monetized?
Consumer chatbot	Individual	Subscription
Advertising AI	Advertiser	User attention/intention
Enterprise copilot	Employer	Employee productivity
AI coding/agent	Employer/developer	Completed work/outcomes
API	Developer/company	Tokens/compute
Private enterprise AI	Large enterprise/government	Security + customization + integration
AI embedded in cloud	Enterprise	Cloud consumption + software
AI embedded in Search/social/e-commerce	Advertiser/consumer	Advertising/transactions
Open-weight model	Indirectly	Cloud, services, ecosystem, hardware

And three models are developing:

• The standalone AI utility (OpenAI/Anthropic)

• The AI-subsidized platform (Google, Meta and Amazon)

• The specialized enterprise supplier (Cohere, Mistral, also Anthropic). 

Advertising is becoming much more important for utility providers such as OpenAI. OpenAI now describes advertising as one of four pillars alongside consumer subscriptions, enterprise offerings and usage-based APIs.

And SpaceXSI is pursuing something similar, focusing on advertising, subscriptions, data licensing and API access as expected AI revenue streams. 

Supplier	Primary revenue routes	Commercial traction	Profitability	Basic strategy
OpenAI GPT/ChatGPT	Enterprise subscriptions, ChatGPT consumer subscriptions, API, coding/agent products, advertising	Enterprise already represented >40% of revenue in Q1 2026; enterprise and consumer revenue reportedly reached parity ahead of schedule. ChatGPT advertising reached a $1B annualized run rate within 200 days. (OpenAI)	Still deeply investment-heavy. OpenAI reportedly expects enormous negative free cash flow through 2030 despite rapidly rising revenue. (Reuters)	Build a diversified AI platform: consumer + enterprise + API + ads, with increasingly expensive agents and specialized applications providing higher-value revenue.
Anthropic Claude	Enterprise subscriptions, Claude Code, API/model usage, customized enterprise deployments	More than 500 customers spend >$1M annually, up from 12 two years earlier. Claude Code alone had >$2.5B run-rate revenue, with enterprise accounting for >half of Claude Code revenue. (Anthropic)	Closest of the frontier startups to demonstrated profitability. It was projected to generate $10.9B Q2 revenue and $559M operating profit. (Financial Times)	Enterprise-first. Sell AI as a high-value productivity/automation tool rather than primarily as a consumer chatbot.
Google Gemini	Search advertising, Google Cloud, Gemini Enterprise, Workspace, consumer subscriptions, API usage	Gemini models process about 22B API tokens/minute; nearly 90% of Fortune 100 companies use Gemini Enterprise. Google Cloud Q2 revenue rose 82%, driven by AI infrastructure and solutions.	Already highly profitable at corporate level, although Google does not separately disclose Gemini profitability. Alphabet Q2 operating margin was 34%.	Monetize AI everywhere. Gemini does not have to generate a standalone profit if it increases Search, Cloud, Workspace and advertising economics.
Meta  Llama / Meta AI	Primarily advertising; potentially enterprise/business AI and future paid products	Meta's Family of Apps generated $60.8B revenue in Q2 2026, with ad impressions +14% and average ad price +12%. Meta says AI is already accelerating its core business. (Meta)	AI does not need to be separately profitable. Meta itself is highly profitable, although AI investment is putting substantial pressure on costs.	Give away the model → increase engagement → improve advertising and eventually monetize enterprise/agent applications.
SpaceXSI	Grok subscriptions, X subscriptions, advertising, API, data licensing, enterprise AI	By March 2026, xAI/X reported 1.9M SuperGrok/Grok paid subscribers and 6.3M total paid subscribers including X Premium. AI revenue was $818M in Q1 2026. (SEC)	Large losses. xAI reportedly lost $6.4B in 2025 on $3.2B revenue; Q1 2026 AI operating loss was $2.47B. (TechCrunch)	Consumer distribution + subscriptions + ads + data. The unusual asset is X's huge installed audience and real-time data stream.
Mistral Le Chat, Mistral models	Enterprise contracts, model/API usage, private deployments, customization, model hosting	>125 customers; company said it was on track for $1B annual recurring revenue in 2026. (MarketScreener UK)	Not yet established as profitable. Continues to raise large amounts of capital, including a €3B 2026 round. (MarketScreener UK)	European enterprise AI provider: open/customizable models + long-term strategic contracts + sovereign/private AI infrastructure.
Cohere Command, North	Private enterprise deployments, customized models, enterprise software	Already had $100M annualized revenue in 2025; ~85% of business came from private deployments, with reported margins around 80%. (Reuters)	Not publicly demonstrated as profitable.	Avoid the consumer chatbot race. Concentrate on regulated industries and private, customized AI where customers will pay for security and control.
DeepSeek V4	API usage, consumer app, potentially licensing/enterprise usage	API revenue reached about $70.7M in the first seven months of 2026, roughly 10× 2025. API gross margin reportedly reached 82.9%. (The Information)	Still losing money: approximately $101M net loss in the first seven months of 2026, despite strong API gross margins. (The Information)	Extreme cost efficiency + low prices + volume. Its current pricing is dramatically below many U.S. frontier models. (DeepSeek API Docs)
Amazon Nova	AWS consumption, Bedrock, enterprise AI, advertising/e-commerce productivity	Amazon said its AI business within AWS had surpassed a $25B annual revenue run rate and was growing triple digits.	AI benefits an already profitable AWS business rather than needing standalone model profitability.	Sell the infrastructure around AI. Nova makes AWS/Bedrock more competitive and encourages customers to consume more AWS compute and services.